Computer-implemented system and method of facilitating artificial intelligence based revenue cycle management in healthcare

ABSTRACT

A system and method of facilitating revenue cycle management in healthcare are disclosed. A voice module records a physician treating a patient. A speech to text engine converts the recorded voice into a text format to generate a transcription file. The rules are based on the outcome/learning of the artificial intelligence engine to processes the text to automatically populate data field(s) associated with the diagnosis and treatment in a transcription template. The artificial intelligence engine processes document(s) associated with the diagnosis and treatment of the patient to identify code(s) and populating coding field(s) with the code(s). The rules from the dynamic rules engine are used to processes data records associated with the patient to automatically populate field(s) in an insurance claim form of an insurance claim associated with the patient, and identify a payor to be billed with an appropriate amount determined based upon the code(s) identified.

TECHNICAL FIELD

The present disclosure described herein, in general, relates to arevenue cycle management in healthcare, and more particularly, relatesto a computer implemented system and method of revenue cycle managementin healthcare using artificial intelligence and machine learningtechniques.

BACKGROUND

In a typical revenue cycle management of healthcare systems facilitatinginsurance reimbursements, there exists phases including a transcriptionphase, a coding phase, a billing phase, a payment posting phase and adenial management phase. In the transcription phase, a doctor orphysician dictates treatment or consultation provided to a patientwherein such dictation is recorded in the form of a transcription filealso known as an Electronic Medical Record. Usually, the transcriptionfile is manually generated by a transcriptionist who listens to andtranscribes the dictation, resulting in a transcription that is prone toerrors. The physician/doctor needs to manually rectify the errors in thetranscription file before being finalized and sending to the codingphase. In the coding phase, a dedicated coder team is employed foranalyzing the consultation, diagnosis and treatment provided by thedoctor or physician to the patient based upon the contents in thetranscription file. The analysis of the diagnosis and treatment isperformed by the coder team to map one or more codes from an ICD(International classification of diseases) library storing a pluralityof codes.

Since, the codes are mapped manually based upon the transcription file,there is a high probability of incorrect mapping of the one or morecodes with the diagnosis not only due to the errors caused in thegeneration of the transcription file, but also, due to manual mapping ofthe codes from the ICD library by the coder team. The one or more codesmapped to an insurance master/fee schedule are used in the billing phaseto determine an appropriate insurance amount to be paid to the doctor orphysician by a payor (i.e. an insurer) on behalf of the patient (i.e. aninsurer) and confirm the payment in the payment posting phase. Since thebilling is based upon the one or more codes mapped from the ICD library,the incorrect mapping of the one or more codes results in determining aninappropriate insurance amount. In other words, the insurance claimed bythe doctor/physician is either underpaid or overpaid which furtherresults in denial of the insurance claim by the payor. The denial ofinsurance claim may further be due to errors in recording the diagnosis& treatment of the patient, and mapping of codes as-well-as inidentifying the payor for payment to the doctor or physician.

Therefore, in a current scenario, various phases associated with therevenue cycle management including the transcription phase, the codingphase, the billing phase, and the payment posting phase are operated indifferent fragments and there is no interconnection between individualphases. Further, since human resources are employed for implementingvarious processes associated with the revenue cycle management, asubstantial amount of time is wasted in rectifying errors generated atvarious stages of the revenue cycle management thereby resulting inoverall increase in time of the revenue cycle. Further, the probabilityof insurance claims being denied and/or underpaid/overpaid is increaseddue to presence of the errors not being rectified.

SUMMARY

This summary is provided to introduce aspects related to computerimplemented systems and methods of facilitating revenue cycle managementin healthcare and are further described below in the detaileddescription. This summary is not intended to identify essential featuresof the subject matter nor is it intended for use in determining orlimiting the scope of the subject matter.

In one embodiment, a computer implemented system of facilitating revenuecycle management (RCM) in healthcare is disclosed. The system mayinclude a processor and a memory coupled with the processor. Theprocessor may execute a plurality of modules stored in the memory. Theplurality of modules may include a voice recording module, a speech totext engine, dynamic rules engine, an artificial intelligence engine andan interactive dashboard module. The processor may execute the voicerecording module for recording a voice of a physician treating apatient, wherein the voice recorded is associated with a diagnosis andtreatment for the patient. The processor may further execute a speech totext engine for converting the voice recorded into a text format togenerate a transcription file. Further, the processor may execute rulesfrom the dynamic rules engine which sets the rules based on theoutcome/learning of the artificial intelligence for processing the textin the transcription file to automatically populate one or more datafields associated with the diagnosis and treatment in a transcriptiontemplate selected from a plurality of predefined templates. Theprocessor may further execute the artificial intelligence engine forprocessing one or more documents associated with the diagnosis andtreatment of the patient to identify the diagnosis and provide clinicaldecision support where needed. Further, the processor may furtherexecute the artificial engine for processing the one or more documentsto also provide one or more codes from a plurality of codes pre-storedin the memory and populating one or more coding fields with the said oneor more codes. Further, the processor may execute the rules from thedynamic rules engine which sets the rules based on the outcome/learningof the artificial intelligence engine for processing data recordsassociated with the patient to automatically populate one or more fieldsin an insurance claim form of an insurance claim associated with thepatient and identify a payor to be billed with an appropriate amountdetermined based upon the one or more codes identified. In accordancewith an aspect of this embodiment, the processor may further execute therules from the dynamic rules engine which sets the rules based on theoutcome/learning of the artificial intelligence engine to compareexplanation of benefits (EOB) associated with the insurance claim with apredefined master file to detect the insurance claim being underpaid,overpaid, or denied. The insurance claim being detected as underpaid,overpaid, or denied is further re-assessed by the artificialintelligence engine to correct the deficiencies associated with theinsurance claim and thereby re-billing the insurance claim. The systemmay further include a claim processing module stored in the memory toconfirm and validate the processing of the insurance claim associatedwith the patient and posting the insurance claim for payment to thepayor. Further, the system may include the interactive dashboard moduleto display, on a user device associated with the physician, the entireRCM workflow and a financial status, and further one or more of thetranscription template populated with the one or more data fields, acharge entry form populated with the one or more codes to be submittedto a clearing house or the payor, and an indicative amount to be paid bythe payor, wherein the indicative amount is populated and displayedbased upon the data from the transcription template and the charge entryform. Furthermore, the system may include a machine learning module forcontinuously training the artificial intelligence engine based uponhistorical data associated with transcription, coding, billing,over-payment, under-payment, or denial of prior insurance claims.

In another embodiment, a computer implemented method of facilitatingrevenue cycle management (RCM) in healthcare is disclosed. The methodmay include recording, via a processor, a voice of a physician treatinga patient, wherein the voice recorded is associated with a diagnosis andtreatment for the patient. Further, the method may include converting,via the processor, the voice recorded into a text format to generate atranscription file. Further, the method may include processing, via theprocessor, the text in the transcription file to automatically populateone or more data fields associated with the diagnosis and treatment in atranscription template selected from a plurality of predefinedtemplates. The method may further include processing, via the processor,one or more documents associated with the diagnosis and treatment of thepatient to identify one or more codes from a plurality of codespre-stored in a memory coupled with the processor and populating one ormore coding fields with the said one or more codes. Further, the methodmay include processing, via the processor, data records associated withthe patient to automatically populate one or more fields in an insuranceclaim form of an insurance claim associated with the patient andidentify a payor to be billed with an appropriate amount determinedbased upon the one or more codes identified. In accordance with aspectof this embodiment, the method may further include comparing, via theprocessor, explanation of benefits (EOB) associated to the insuranceclaim with a predefined master file to detect the insurance claim beingunderpaid, overpaid, or denied. The insurance claim being detected asunderpaid, overpaid, or denied may be further re-assessed by theartificial intelligence engine to correct the deficiencies associatedwith the insurance claim and thereby re-billing the insurance claim. Themethod may further include confirming and validating, via the processor,the processing of the insurance claim associated with the patient andposting the insurance claim for payment to the payor. The method mayfurther include displaying, via the processor, on a user deviceassociated with the physician, the entire RCM workflow and a financialstatus, and further one or more of the transcription template populatedwith the one or more data fields, a charge entry form populated with theone or more codes to be submitted to a clearing house or the payor, andan indicative amount to be paid by the payor, wherein the indicativeamount is populated and displayed based upon the data from thetranscription template and the charge entry form. Furthermore, themethod may include continuously training the processor based uponhistorical data associated with transcription, coding, billing,over-payment, under-payment, or denial of prior insurance claims.

In yet another embodiment, non-transitory computer readable mediumstoring a program of facilitating revenue cycle management (RCM) inhealthcare is disclosed. The program may include programmed instructionsfor recording a voice of a physician treating a patient, wherein thevoice recorded is associated with a diagnosis and treatment for thepatient. Further, the program may include programmed instructions forconverting the voice recorded into a text format to generate atranscription file. Further, the program may include programmedinstructions for processing the text in the transcription file toautomatically populate one or more data fields associated with thediagnosis and treatment in a transcription template selected from aplurality of predefined templates. The program may further includeprogrammed instructions for processing one or more documents associatedwith the diagnosis and treatment of the patient to identify one or morecodes from a plurality of codes that are pre-stored in a memory andpopulating one or more coding fields with the said one or more codes.Further, the program may include programmed instructions for processingdata records associated with the patient to automatically populate oneor more fields in an insurance claim form of an insurance claimassociated with the patient and identify a payor to be billed with anappropriate amount determined based upon the one or more codesidentified. In accordance with an aspect of this embodiment, the programmay further include programmed instructions for comparing explanation ofbenefits (EOB) associated with the insurance claim with a predefinedmaster file to detect the insurance claim being underpaid, overpaid, ordenied. The insurance claim being detected as underpaid, overpaid, ordenied may be re-assessed by the artificial intelligence engine tocorrect the deficiencies associated with the insurance claim and therebyre-billing the insurance claim. The program may further includeprogrammed instructions for confirming and validating the processing ofthe insurance claim associated with the patient and posting theinsurance claim for payment to the payor. Further, the program mayinclude programmed instructions for displaying, on a user deviceassociated with the physician, the entire RCM workflow and a financialstatus, and further one or more of the transcription template populatedwith the one or more data fields, a charge entry form populated with theone or more codes to be submitted to a clearing house or the payor, andan indicative amount to be paid by the payor, wherein the indicativeamount is populated and displayed based upon the data from thetranscription template and the charge entry form. Furthermore, theprogram may include programmed instructions for continuously trainingthe program based upon historical data associated with transcription,coding, billing, over-payment, under-payment, or denial of priorinsurance claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is described with reference to the accompanyingfigures. In the figures, the left-most digit(s) of a reference numberidentifies the figure in which the reference number first appears. Thesame numbers are used throughout the drawings to refer to like featuresand components.

FIG. 1 is a diagram of a network implementation of a system offacilitating revenue cycle management in healthcare, in accordance withan embodiment of the present disclosure.

FIG. 2 is a diagram of the system, in accordance with an embodiment ofthe present disclosure.

FIG. 3(a), FIG. 3(b), FIG. 3(c) and FIG. 3(d) are flow diagramsdepicting working of an artificial intelligence engine and a claimprocessing module within the system 102, in accordance with anembodiment of the present disclosure.

FIG. 4 is a flow diagram depicting working of a machine learning modulewithin the system 102, in accordance with an embodiment of the presentdisclosure.

FIG. 5(a) and FIG. 5(b) are flow diagrams depicting a method offacilitating revenue cycle management in healthcare, in accordance withan embodiment of the present disclosure.

DETAILED DESCRIPTION

Some embodiments of this disclosure, illustrating all its features, willnow be discussed in detail. The words “comprising,” “having,”“containing,” and “including,” and other forms thereof, are intended tobe equivalent in meaning and be open ended in that an item or itemsfollowing any one of these words is not meant to be an exhaustivelisting of such item or items, or meant to be limited to only the listeditem or items.

It must also be noted that, the singular forms “a,” “an,” and “the”include plural references unless the context clearly dictates otherwise.Although any methods similar or equivalent to those described herein canbe used in the practice or testing of embodiments of the presentdisclosure, the exemplary methods are now described. The disclosedembodiments are merely exemplary of the disclosure, which may beembodied in various forms.

Various modifications to the embodiment will be readily apparent tothose skilled in the art and the generic principles herein may beapplied to other embodiments. However, one of ordinary skill in the artwill readily recognize that the present disclosure is not intended to belimited to the embodiments illustrated, but is to be accorded the widestscope consistent with the principles and features described herein.

While aspects of the described system and method of facilitating revenuecycle management in healthcare may be implemented in any number ofdifferent computing systems, environments, and/or configurations, theembodiments may be described in the context of the following exemplarysystem.

Referring now to FIG. 1, a network implementation 100 of a system 102 offacilitating revenue cycle management in healthcare is shown, inaccordance with an embodiment of the present disclosure. Although thepresent subject matter is explained considering that the system 102 isimplemented on a server, it may be understood that the system 102 mayalso be implemented in a variety of computing systems, such as a laptopcomputer, a desktop computer, a notebook, a workstation, a mainframecomputer, a server, a network server, a cloud-based computingenvironment and the like. In one implementation, the system 102 may beimplemented in a cloud-based computing environment. It will beunderstood that the system 102 also may be accessed by multipleregistered users through one or more user devices 104-1, 104-2, 104-3,or 104-N. Examples of the user devices 104 may include, and are notlimited to, a portable computer, a personal digital assistant, ahandheld device, and a workstation. Further, the system 102 may becommunicatively coupled with the user devices 104-1, 104-2, 104-3, or104-N, through a network 106.

In one implementation, the network 106 may be a wireless network, awired network or a combination thereof. The network 106 can beimplemented as one of the different types of networks, such as intranet,local area network (LAN), wide area network (WAN), the internet, and thelike. The network 106 may either be a dedicated network or a sharednetwork. The shared network represents an association of the differenttypes of networks that use a variety of protocols, for example,Hypertext Transfer Protocol (HTTP), Transmission ControlProtocol/Internet Protocol (TCP/IP), Wireless Application Protocol(WAP), a telecommunication network (e.g. 2G/3G/4G/5G) and the like, tocommunicate with one another. Further the network 106 may include avariety of network devices, including routers, bridges, servers,computing devices, storage devices, and the like.

Referring now to FIG. 2, the system 102 is illustrated in accordancewith an embodiment of the present disclosure. In one embodiment, thesystem 102 may include a processor 202, an input/output (I/O) interface204, and a memory 206. The processor 202 may be implemented as one ormore microprocessors, microcomputers, microcontrollers, digital signalprocessors, central processing units, state machines, logic circuitries,and/or any devices that manipulate signals based on operationalinstructions. Among other capabilities, the processor 202 is configuredto fetch and execute computer-readable instructions stored in the memory206.

The I/O interface 204 may include a variety of software and hardwareinterfaces, for example, a web interface, a graphical user interface,and the like. The I/O interface 204 may allow the system 102 to interactwith the user/consumer directly or through the consumer devices 104.Further, the I/O interface 204 may enable the system 102 to communicatewith other computing devices, such as web servers and external dataservers (not shown). The I/O interface 204 can facilitate multiplecommunications within a wide variety of networks and protocol types,including wired networks, for example, LAN, cable, etc., and wirelessnetworks, such as WLAN, cellular, or satellite. The I/O interface 204may include one or more ports for connecting a number of devices to oneanother or to another server.

The memory 206 may include any computer-readable medium and computerprogram product known in the art including, for example, volatilememory, such as static random access memory (SRAM) and dynamic randomaccess memory (DRAM), and/or non-volatile memory, such as read onlymemory (ROM), erasable programmable ROM, flash memories, hard disks,optical disks, and magnetic tapes. The memory 206 may include modules208 and data 224.

The modules 208 include routines, programs, objects, components, datastructures, etc., which perform particular tasks or implement particularabstract data types. In one implementation, the modules 208 may includea voice recording module 210, a speech to text engine 212, an artificialintelligence engine 214, a machine learning module 216, a claimprocessing module 218, dynamic rules engine 220, an interactivedashboard module 222, and other modules (not shown). The other modulesmay include programs or coded instructions that supplement applicationsand functions of the system 102.

The data 224, amongst other things, serves as a repository for storingdata processed, received, and generated by one or more of the modules208. The data 224 may also include a data repository 226 and other data228. The other data 228 may include data generated as a result of theexecution of one or more modules in the other modules. The system 102may be accessed by the user device 104 registered with the system 102.The user device 104 may belong to a physician or a doctor. Further, eachof the aforementioned modules is explained in subsequent paragraphs ofthe specification.

Now referring to FIG. 2, the voice recording module 210 may record avoice or an audio of a physician or a doctor (hereafter referred asphysician) treating a patient. The physician or the doctor may dictate adiagnosis or a treatment via the user device 104. The diagnosis andtreatment dictated by the physician may be recorded by the voicerecording module 210. The voice or audio recorded by the voice recordingmodule 210 may be stored in the data repository 226.

Based upon the recording of the voice of the physician, the speech totext engine 212, as shown in FIG. 2, may be configured to covert thevoice into text format. A person skilled in the art would easily realizeand appreciate that the speech to text engine 212 may use advanced deeplearning functionalities of automatic speech recognition (ASR)technologies for converting speech to text format. Specifically, thespeech to text engine 212 may convert voice/audio associated with eachof the diagnosis and treatment into textual format to generate atranscription file. For the purposes of this disclosure, a transcriptionfile is a textual file capturing the treatment, diagnosis orconsultation given to the patient by the physician. Further, as shown inFIG. 2, the artificial intelligence engine 214 may be configured toautomatically correct the textual data in the transcription file. Itmust be understood that the artificial intelligence engine 214 may beadaptively trained via the machine learning module 216 to rectify theerrors in the textual data in the transcription file. The transcriptionfile corrected may be stored in the data repository 226.

In one embodiment, as shown in FIG. 2, the artificial intelligenceengine 214 may be configured to process the textual data in thetranscription file. The artificial intelligence engine 214 may beconfigured to process the text in order to automatically populate one ormore data fields in a template selected from a plurality of predefinedtemplates stored in the data repository 226. Each template of theplurality of templates is associated with different stages of thediagnosis and treatment of the patients. For example, the template maybe associated with initial consultation, diagnosis detected, treatmentssuggested, or discharge summary. For each different template, differentdata fields may be required to be populated in the respective template.In one embodiment, each predefined template may indicate an electronicmedical record (EMR) or electronic health record (EHR) for the patientbeing treated by the physician. In one embodiment, the one or more datafields in the predefined template may include vitals, diagnosis, andtreatment etc. The rules from the dynamic rules engine 220 which setsthe rules based on the outcome/learning of the artificial intelligenceengine 214 automatically populates the one or more fields withcorresponding text from the transcription file. In one embodiment, adata field “vitals” may be populated with blood pressure, temperature,etc. Further, a data field “diagnosis” may be populated with textcorresponding to the diagnosis dictated by the physician. A personskilled in the art would easily realize and appreciate that theartificial intelligence engine 214 may populate the one or more datafields with the text in the transcription file using text processingtechniques such as Natural Language Processing (NLP), semantic and/orsyntactic analysis, and the like. FIG. 3(a) illustrates a methodimplemented by the artificial intelligence engine 214 for automaticallypopulating the one or more data fields in the template selected from theplurality of predefined templates stored in the data repository 226.

As shown in FIG. 3(a), at step 302, the voice recording module 210 mayrecord a voice dictated by the physician. At step 304, an indication ofa template, one or more codes, and chief complaint selected by thephysician via the user device associated with the physician may bereceived by the system. At step 306, the speech is converted into a textformat via the speech to text engine 212. At step 308, the artificialengine mines previous EMR data and correlates the previous EMR data withthe chief complaint to suggest an actionable plan to thephysician/doctor and help the physician/doctor with the clinicaldecision support. At step 310, the artificial intelligence engine 214organizes sorts and merges the data from the text into sequentialheadings as per the template selected from the plurality of predefinedtemplates. Specifically, at step 310, the artificial intelligence engine214 compares the text in the transcription file to the built-in headingsin the template selected. In one example, the name of the patient may bepopulated in the “Patient Name” column, chief complaint of the patientmay be populated in the “Chief Complaint” heading etc. as per thetemplate selected. Further, the artificial intelligence engine 214merges the full text with the built-in headings as per the templateselected. Specifically, after all the headings in the template arepopulated with the respective text, the artificial intelligence engine214 merges them in the form of a single clinical document. A script(programmed instructions) associated with the artificial intelligenceengine 214 is re-run to check with the output. Finally, at the end ofstep 310, template output format is obtained. At step 312, theartificial intelligence engine 214 automatically populates therespective fields in the transcription template (i.e. the templateoutput format). At step 314, a completed transcription file based uponverification of the transcription template by the transcription qualityassurance (QA) team may be generated. The machine learning module 216may monitor the changes made in the completed transcription file ascompared to the transcription template and train the artificialintelligence engine 214 to adapt the changes made in the transcriptiontemplate.

Now referring to FIG. 2, the artificial intelligence engine 214 mayprocess the one or more documents (including the clinical document)associated with the diagnosis and treatment of the patient in order toidentify one or more codes from a plurality of codes within an ICDlibrary stored in the data repository 226. Each code of the plurality ofcodes is associated with a specific diagnosis and specific treatment andfurther having an associated appropriate monetary amount. The one ormore codes identified may be populated in one or more coding fields.FIG. 3(b) illustrates a method implemented by the artificialintelligence engine 214 for automatically populating the one or morecoding fields in the transcription template.

As shown in FIG. 3(b), at step 316, an indication of the clinicaldocuments associated with the consultation, diagnosis and treatment forthe patient being verified by physician may be received by the system.At step 318, analysis of the clinical documents is initiated. At step320, the clinical documents are analyzed for the treatment anddiagnosis. Specifically, the artificial intelligence engine 214processes the clinical documents and extracts data associated with thediagnosis provided. The data extracted is compared with built-in ICD-10codes for (Current Procedural Terminology) CPT and diagnosis and mappingthe data with one or more ICD-10 codes. A script (programmedinstructions) associated with the artificial intelligence engine 214 isre-run to check with the output. Finally, at the end of step 320, codesmapped are obtained as an output. At step 322, the codes mapped areautomatically populated in respective fields in coding CPT & diagnosisfields. At step 324, an indication of the accuracy of the codes mappedbeing verified by a coding quality assurance (QA) team may be receivedby the system. The machine learning module 216 may monitor the changesmade in the coding fields based upon the verification of the accuracy ofthe codes and train the artificial intelligence engine 214 to adapt thechanges made in the coding fields.

Now again referring to FIG. 2, the artificial intelligence engine 214may further process data records associated with the patient toautomatically populate one or more fields in an insurance claim form ofan insurance claim associated with the patient and identify a payor tobe billed with an appropriate amount determined based upon the one ormore codes identified. FIG. 3(c) illustrates a method implemented by theartificial intelligence engine 214 for automatically populating the oneor more fields in the insurance claim form associated with the patientand to identify the payor for processing the appropriate insured amount.

As shown in FIG. 3(c), at step 326, an indication of the code chart,depicting the codes mapped with the treatment and diagnosis of thepatient, being verified and approved by the physician may be received inthe system. At step 328, the patient's records are verified by thesystem for completeness of the patient's records. At step 330, a payorto be billed is identified. At step 332, an insurance bill is createdand run through the built-in claim verifier. A batch file is generatedfor the day. In the process of creating the insurance bill, theartificial intelligence engine 214 may process the patient's records toidentify the payor to be billed. Further, the artificial intelligenceengine 214 processes the patient's records to automatically populate theone or more fields in the insurance claim form with requiredinformation. A script (programmed instructions) associated with theartificial intelligence engine 214 is re-run to check with the output.Finally, at the end of step 332, a claim file is obtained as an outputand included in the batch file. At step 334, an indication of the batchfile being verified by a billing quality assurance (QA) team may bereceived in the system. The machine learning module 216 may monitor thechanges made by the billing QA team in the batch file based upon theverification of the batch file and train the artificial intelligenceengine 214 to adapt the changes made in the batch file. At step 336, thebatch file is uploaded to a connected clearing house for processing theinsurance amount claimed.

Now again referring to FIG. 2, the artificial intelligence engine 214may further compare explanation of benefits (EOB) associated to theinsurance claim with a predefined master file to detect the insuranceclaim being underpaid, overpaid, or denied. The master file may includea fee schedule available for the insurance claims. In one embodiment,the master file includes fees that facilities may normally charge for aparticular treatment or diagnosis and a standard fee schedule receivedfrom multiple insurance companies. FIG. 3(d) illustrates a methodimplemented collectively by the artificial intelligence engine 214 andthe claim processing module 218 for comparing explanation of benefits(EOB) associated to the insurance claim with the predefined master fileto detect the insurance claim being underpaid, overpaid, or denied andthereby process the claim accordingly.

As shown in FIG. 3(d), at step 338, the EOB may be received from theInsurance team. At step 340, the EOB may be uploaded in the system 102.At step 342, the EOB may be passed through the insurance charge masterand insurance master (i.e. the master file) stored in the datarepository 226. At step 344, the claim received may be subjected toanalysis. At step 346, the EOB may be compared with the insurance chargemaster and insurance master in order to identify whether the insuranceclaim is over-paid, under-paid or denied. The analysis at step 346 mayinclude comparing the insurance charge master and the insurance masterwith the EOB. The analysis at step 346 may further include segregatingthe insurance claim into either denied, underpaid, or overpaid. A script(programmed instructions) associated with the artificial intelligenceengine 214 is re-run to check with the output. Finally, at the end ofstep 346, an inventory associated with the insurance claim is preparedand sent to the Accounts Receivable (AR) team. At step 348, theinsurance claim if detected as denied/underpaid may be pooled intodenied or underpaid buckets. At step 350, the denied/underpaid claim maybe re-assessed by the artificial intelligence engine 214 to correct thedeficiencies and/or incorrect information associated with the insuranceclaim and thereby re-bills the insurance claim. In some embodiments, theinsurance claim may be allocated to the Accounts Receivable (AR) team tomanually connect with the insurance company in order tofollow-up/adjudicate/re-bill. The AR/billing team may reassess theinsurance claim and thereby correct the deficiencies associated with theinsurance claim. At step 352, based upon the adjudication or re-billingof the insurance claim, the claim processing module 218 may confirm andvalidate the processing of the insurance claim associated with thepatient. Further, at step 354, the insurance claim for payment may beposted to the payor.

As explained above, the outputs of each of the aforementioned steps,performed by the artificial intelligence engine 214, including thetranscription template, the codes mapped, claim insurance form and theclaim processing may be changed and/or rectified via manualinterventions. These changes may be monitored by the machine learningmodule 216 and accordingly train the artificial intelligence engine 214to adapt with these changes for determining error-free outputs infuture. Referring to FIG. 4 is a flow diagram depicting working of amachine learning module 216 within the system 102, in accordance with anembodiment of the present disclosure.

As shown in FIG. 4, at step 402 and step 416, the historical data thatincludes the existing Transcription, Coding, Billing and AR informationavailable in the system along with Feedback data received from theArtificial Intelligence (AI) engine 214 is taken into consideration toanalyze and re-train the system at step 418. The system achieves thisprocess by creating a Machine Learning (ML) Pipeline at step 404 whichis build by required data extracted and cleaned-up from the Historicaldata and the Feedback data. Once the data clean-up and re-training iscompleted; the system uses this data to update the existing program andupdates the program accordingly at step 406/420 to fine tune the system.The program is then deployed at step 408/422 into the main program whichis then funneled into scoring the results at step 412. Scoring is theprocess implemented to ensure the re-programming performed from dataused by the historical data, the feedback data and day to dayoperational data 410 related to transcription, coding, billing and AR isable to achieve better results. The system continuously monitors at step414 the data and its performance in a live environment. Further to this,the results derived from the monitoring step is then passed back to theAI engine 214, which further feeds into the feedback data and the cyclecontinues.

Now referring to FIG. 5(a) and FIG. 5(b), a method 500 of facilitatingrevenue cycle management in healthcare is shown, in accordance with anembodiment of the present subject matter. The method 500 may bedescribed in the general context of computer executable instructions.Generally, computer executable instructions can include routines,programs, objects, components, data structures, procedures, modules,functions, etc., that perform particular functions or implementparticular abstract data types. The method 500 may also be practiced ina distributed computing environment where functions are performed byremote processing devices that are linked through a communicationsnetwork. In a distributed computing environment, computer executableinstructions may be located in both local and remote computer storagemedia, including memory storage devices.

The order in which the method 500 is described is not intended to beconstrued as a limitation, and any number of the described method blockscan be combined in any order to implement the method 500 or alternatemethods. Additionally, individual blocks may be deleted from the method500 without departing from the spirit and scope of the disclosuredescribed herein. Furthermore, the method can be implemented in anysuitable hardware, software, firmware, or combination thereof. However,for ease of explanation, in the embodiments described below, the method500 may be considered to be implemented in the above described in thesystem 102.

At block 502, a voice of a physician treating a patient may be recorded.The voice recorded may be associated with a diagnosis and treatment forthe patient. In an implementation, the voice of the physician may berecorded via the voice recording module 210 of the system 102.

At block 504, the voice recorded may be converted into a text format togenerate a transcription file. In an implementation, the voice may beconverted into the text format via the speech to text engine 212 of thesystem 102.

At block 506, the text in the transcription file may be processed toautomatically populate one or more data fields associated with thediagnosis and treatment in a transcription template. In animplementation, the text in the transcription file may be processed viathe artificial intelligence engine 214 of the system 102.

At block 508, one or more documents associated with the diagnosis andtreatment of the patient may be processed to identify one or more codesfrom a plurality of codes pre-stored in the memory and populating one ormore coding fields with the said one or more codes. In animplementation, the one or more documents associated with the diagnosisand treatment of the patient may be processed via the artificialintelligence engine 214 of the system 102.

At block 510, data records associated with the patient may be processedto automatically populate one or more fields in an insurance claim formof an insurance claim associated with the patient and identify a payorto be billed with an appropriate amount determined based upon the one ormore codes identified. In an implementation, the data records associatedwith the patient may be processed via the artificial intelligence engine214 of the system 102.

At block 512, explanation of benefits (EOB) associated to the insuranceclaim may be compared with a predefined master file to detect theinsurance claim being underpaid, overpaid, or denied. In animplementation, the EOB may be compared with the predefined master filevia the artificial intelligence engine 214 of the system 102.

At block 514, the insurance claim being detected as underpaid, overpaid,or denied may be reassessed by the artificial intelligence engine 214 inorder to correct/rectify the deficiencies associated with the insuranceclaim and thereby re-billing the insurance claim. In an alternativeembodiment, the insurance claim may be allocated to the AccountsReceivable (AR) team to manually connect with the insurance company inorder to follow-up/adjudicate/re-bill.

At block 516, the processing of the insurance claim associated with thepatient may be confirmed, validated and posted for payment to the payor.In an implementation, the insurance claim insurance claim associatedwith the patient may be confirmed, validated and posted for payment tothe payor via the claim processing module 218 of the system 102.

At block 518, the interactive dashboard module 222 of the system 102 maydisplay, on a user device associated with the physician, the entire RCMworkflow and a financial status, and further one or more of thetranscription template populated with the one or more data fields, acharge entry form populated with the one or more codes to be submittedto a clearing house or the payor, and an indicative amount to be paid bythe payor. In accordance with aspects of the disclosure, the indicativeamount may be populated and displayed based upon the data from thetranscription template and the charge entry form. It must be understoodthat the financial status herein includes the current status/stage ofthe claim. Further, the charge entry form includes the one or more codesadded in the medical coding stage which is further populated in theinsurance claim form (i.e. the information to be populated toelectronically submit the claim).

At block 520, the artificial intelligence engine 214 may be trainedbased upon historical data associated with transcription, coding,billing, over-payment, under-payment, or denial of prior insuranceclaims. In an implementation, the artificial intelligence engine 214 maybe trained via the machine learning module 216 of the system 102.

Although implementations for method(s) and system(s) of facilitatingrevenue cycle management in healthcare have been described in languagespecific to structural features and/or methods, it is to be understoodthat the implementations and/or embodiments are not necessarily limitedto the specific features or methods described. Rather, the specificfeatures and methods are disclosed as examples of implementations formethod and system of facilitating revenue cycle management inhealthcare.

What is claimed is:
 1. A computer implemented system of facilitatingrevenue cycle management in healthcare, the system comprising: aprocessor; and a memory coupled with the processor, wherein theprocessor executes a plurality of modules stored in the memory, theplurality of modules comprising: a voice recording module configured torecord a voice of a physician treating a patient, wherein the voicerecorded is associated with a diagnosis and treatment for the patient; aspeech to text engine configured to convert the voice recorded into atext format to generate a transcription file; a dynamic rules engineconfigured to execute in tandem with an artificial intelligence engineto process and analyze the text in the transcription file toautomatically populate one or more data fields associated with thediagnosis and treatment in a transcription template selected from aplurality of predefined template; process one or more documentsassociated with the diagnosis and treatment of the patient to provideclinical decision support, and identify one or more codes from aplurality of codes pre-stored in the memory to populate one or morecoding fields with the said one or more codes; and process data recordsassociated with the patient to automatically populate one or more fieldsin an insurance claim form of an insurance claim associated with thepatient and identify a payor to be billed with an appropriate amountdetermined based upon the one or more codes identified.
 2. The system ofclaim 1, wherein the artificial intelligence engine is configured toauto-correct the text obtained from the speech to text engine based uponan input received from the machine learning module.
 3. The system ofclaim 2, wherein the of the artificial intelligence engine is configuredto process the text in the transcription file by organizing, sorting andmerging textual data in the transcription file into sequential headingsas per the transcription template selected.
 4. The system of claim 3,wherein the artificial intelligence engine is configured to process theone or more documents associated with the treatment and diagnosis byextracting data from the one or more documents, comparing the dataextracted with the plurality of codes pre-stored in the memory, andmapping the data extracted with the one or more codes of the pluralityof codes.
 5. The system of claim 4, wherein each code of the pluralityof codes is mapped with a predetermined insurance amount to be billedand forwarded to the payor.
 6. The system of claim 1, wherein theartificial intelligence engine is further configured to compareexplanation of benefits (EOB) associated to the insurance claim with apredefined master file to detect the insurance claim being underpaid,overpaid, or denied.
 7. The system of claim 6, wherein the master filecomprises a fee schedule available for the insurance claims.
 8. Thesystem of claim 7, wherein the insurance claim being detected asunderpaid, overpaid, or denied is further reassessed by the artificialintelligence engine in order to correct the deficiencies associated withthe insurance claim and thereby re-bill the insurance claim.
 9. Thesystem of claim 8, wherein the processor further executes a claimprocessing module stored in the memory to confirm and validate theprocessing of the insurance claim associated with the patient andposting the insurance claim for payment to the payor.
 10. The system ofclaim 1 further comprising an interactive dashboard module configured todisplay, on a user device associated with the physician, the entire RCMworkflow and a financial status, and further one or more of thetranscription template populated with the one or more data fields, acharge entry form populated with the one or more codes to be submittedto a clearing house or the payor, and an indicative amount to be paid bythe payor, wherein the indicative amount is populated and displayedbased upon the data from the transcription template and the charge entryform.
 11. The system of claim 9, further comprising a machine learningmodule for continuously training the artificial intelligence enginebased upon historical data associated with transcription, coding,billing, over-payment, under-payment, or denial of prior insuranceclaims.
 12. A computer implemented method of facilitating revenue cyclemanagement in healthcare, the method comprising: recording, via aprocessor, a voice of a physician treating a patient, wherein the voicerecorded is associated with a diagnosis and treatment for the patient;converting, via the processor, the voice recorded into a text format togenerate a transcription file; processing, via the processor, the textin the transcription file to automatically populate one or more datafields associated with the diagnosis and treatment in a transcriptiontemplate selected from a plurality of predefined template; processing,via the processor, one or more documents associated with the diagnosisand treatment of the patient to identify one or more codes from aplurality of codes pre-stored in the memory and populating one or morecoding fields with the said one or more codes; and processing, via theprocessor, data records associated with the patient to automaticallypopulate one or more fields in an insurance claim form of an insuranceclaim associated with the patient and identify a payor to be billed withan appropriate amount determined based upon the one or more codesidentified.
 13. The method of claim 12, further comprisingauto-correcting, via the processor, the text obtained from the speech totext engine based upon an input received from the machine learningmodule.
 14. The method of claim 13, wherein the processing of the textin the transcription file, via the processor, further comprisesorganizing, sorting and merging textual data in the transcription fileinto sequential headings as per the transcription template selected. 15.The method of claim 14, wherein the processing of the one or moredocuments associated with the treatment and diagnosis, via theprocessor, further comprises extracting data from the one or moredocuments, comparing the data extracted with the plurality of codespre-stored in the memory, and mapping the data extracted with the one ormore codes of the plurality of codes.
 16. The method of claim 12,further comprising comparing, via the processor, explanation of benefits(EOB) associated to the insurance claim with a predefined master file todetect the insurance claim being underpaid, overpaid, or denied.
 17. Themethod of claim 16, further comprising re-assessing, via the processor,the insurance claim being detected as underpaid, overpaid, or denied inorder to correct the deficiencies associated with the insurance claimand thereby re-billing the insurance claim.
 18. The method of claim 15,further comprising confirming and validating the processing of theinsurance claim associated with the patient and posting the insuranceclaim for payment to the payor.
 19. The method of claim 12 furthercomprising displaying, via the processor, on a user device associatedwith the physician, the entire RCM workflow and a financial status, andfurther one or more of the transcription template populated with the oneor more data fields, a charge entry form populated with the one or morecodes to be submitted to a clearing house or the payor, and anindicative amount to be paid by the payor, wherein the indicative amountis populated and displayed based upon the data from the transcriptiontemplate and the charge entry form.
 20. The method of claim 16, furthercomprising continuously training the processor based upon historicaldata associated with transcription, coding, billing, over-payment,under-payment, or denial of prior insurance claims.
 21. A non-transitorycomputer readable medium storing program of facilitating revenue cyclemanagement in healthcare, the program comprising programmed instructionsfor: recording voice of a physician treating a patient, wherein thevoice recorded is associated with diagnosis and treatment for thepatient; converting the voice recorded into a text format to generate atranscription file; processing the text in the transcription file toautomatically populate one or more data fields associated with thediagnosis and treatment in a transcription template selected from aplurality of predefined template; processing one or more documentsassociated with the diagnosis and treatment of the patient to identifyone or more codes from a plurality of codes pre-stored in the memory andpopulating one or more coding fields with the said one or more codes;and processing data records associated with the patient to automaticallypopulate one or more fields in an insurance claim form of an insuranceclaim associated with the patient and identify a payor to be billed withan appropriate amount determined based upon the one or more codesidentified.
 22. The non-transitory computer readable medium of claim 21,wherein the program further comprises programmed instructions forauto-correcting the text obtained from the speech to text engine basedupon an input received from the machine learning module.
 23. Thenon-transitory computer readable medium of claim 21, wherein the programfurther comprises programmed instructions for comparing explanation ofbenefits (EOB) associated to the insurance claim with a predefinedmaster file to detect the insurance claim being underpaid, overpaid, ordenied.
 24. The non-transitory computer readable medium of claim 23,wherein the program further comprises programmed instructions forre-assessing the insurance claim being detected as underpaid, overpaid,or denied in order to correct the deficiencies associated with theinsurance claim and thereby re-billing the insurance claim.
 25. Thenon-transitory computer readable medium of claim 24, wherein the programfurther comprises programmed instructions for confirming and validatingthe processing of the insurance claim associated with the patient andposting the insurance claim for payment to the payor.
 26. Thenon-transitory computer readable medium of claim 21, wherein the programfurther comprises programmed instructions for displaying, on a userdevice associated with the physician, the entire RCM workflow and afinancial status, and further one or more of the transcription templatepopulated with the one or more data fields, a charge entry formpopulated with the one or more codes to be submitted to a clearing houseor the payor, and an indicative amount to be paid by the payor, whereinthe indicative amount is populated and displayed based upon the datafrom the transcription template and the charge entry form.
 27. Thenon-transitory computer readable medium of claim 22, wherein the programfurther comprises programmed instructions for continuously training theprogram based upon historical data associated with transcription,coding, billing, over-payment, under-payment, or denial of priorinsurance claims.